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Abnormal chirality in antiferromagnetic resonance modes of van der Waals 2D magnets
The importance of the Autostrain RV technique in the treatment of right ventricular myocardial alterations in patients with breast cancer receiving chemotherapy
Adoption intensity of soil and water conservation techniques in Burkina Faso is influenced by farmers’ preferences for their attributes
Stronger premicrosaccadic sensitivity enhancement for dark contrasts in the primate superior colliculus
Abstract Microsaccades are associated with enhanced visual perception and neural sensitivity right before their onset, and this has implications for interpreting experiments involving the covert allocation of peripheral spatial attention. However, the detailed properties of premicrosaccadic enhancement are not fully known. Here we investigated how such enhancement in the superior colliculus depends on luminance polarity. Rhesus macaque monkeys fixated a small spot while we presented either dark or bright image patches of different contrasts within the recorded neurons’ response fields. Besides replicating premicrosaccadic enhancement of visual sensitivity, we observed stronger enhancement for dark contrasts. This was especially true at moderate contrast levels (such as 20%), and it occurred independent of an individual neuron’s preference for either darks or brights. On the other hand, postmicrosaccadic visual sensitivity suppression was similar for either luminance polarity. Our results reveal an intriguing asymmetry in the properties of perimicrosaccadic modulations of superior colliculus visual neural sensitivity.
Study on the application of brine mixing method in lithium extraction from Zabuye salt lake, Tibet
First-principles study of structural, elastic, electronic, transport properties, and dielectric breakdown of Cs2Te photocathode
Transboundary hydropolitical conflicts and their impact on river morphology and environmental degradation in the Hirmand Basin, West Asia
Particle breakage characteristics of calcareous sand under confined compression tests
Formation and stability of green and low-cost magnetoliposomes of the soy lecithin, stigmasterol, and β-sitosterol for hyperthermia treatments
A data-driven state identification method for intelligent control of the joint station export system
Abstract As a necessary part of intelligent control of a joint station, the automatic identification of abnormal conditions and automatic adjustment of operation schemes need to judge the running state of the system. In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy. Based on the PSO-GWO-BP pressure drop prediction model, the determination method of state identification threshold is established, and a state identification method based on dynamic threshold is proposed, which realizes the intelligent identification of the system operation state by automatically adjusting the threshold. Through the analysis of the production and operation data of the joint station, the abnormal working conditions are successfully identified, and the effectiveness and accuracy of the method are verified. This method not only enhances the ability to discriminate abnormal working conditions but also adaptively adjusts the operation scheme, which effectively improves the intelligence level of the joint station export system.
Masked autoencoder of multi-scale convolution strategy combined with knowledge distillation for facial beauty prediction
A non-associated constitutive model based on yld2004-18p yield criterion and its applications on sheet metal forming analysis
Mechanisms of change in an online acceptance and commitment therapy intervention for insomnia
Abstract Insomnia, i.e., difficulty falling asleep or staying asleep, is a common condition that is connected to many psychological and physical problems. Online-based Acceptance and Commitment Therapy (ACT) has recently been introduced as an option for treating insomnia. However, our understanding is yet limited on what happens during an online ACT intervention or what underlying mechanisms are critical for outcomes. This study addressed this gap by investigating mediators of a brief self-guided online ACT intervention for adults suffering from subclinical and clinical insomnia. A total of 86 adults were randomized to an intervention group (n = 43) or a waitlist control group (n = 43). Mediator models were used to investigate the effects of online ACT on subjective sleep complaints through changes in daytime sleepiness, dysfunctional beliefs, psychological symptoms, mindfulness, and thought suppression. Two models showed significant indirect effects: The online ACT intervention decreased participants’ thought suppression and depressive symptoms, which then decreased subjective sleep complaints. Other models did not yield significant mediating effects. Acceptance and mindfulness-based approaches may serve as viable options for other existing insomnia treatments. Future studies are encouraged to be conducted, especially concerning flexibility and inflexibility processes as possible mechanisms of change in online ACT for insomnia.
Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm
Evolution of intrinsic disorder in the structural domains of viral and cellular proteomes
An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO
Abstract Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to propose VEE-YOLO, a robust and high-performance defect detection model. Firstly, GSConv was introduced to enhance feature extraction in depthwise separable convolution and establish the VOVGSCSP module, emphasizing feature reusability for more effective feature engineering. Secondly, improvements were made to the model’s feature extraction quality by encoding inter-channel information using efficient multi-Scale attention to consider channel importance. Precise integration of spatial structural and channel information further enhanced the model’s overall feature extraction capability. Finally, EIoU Loss replaced CIoU Loss to address bounding box aspect ratio variability and sample imbalance challenges, significantly improving overall detection task performance. The algorithm’s performance was evaluated using a dataset to detect stranded elastic needle defects. The experimental results indicate that the enhanced VEE-YOLO model’s size decreased from 6.096 M to 5.486 M, while the detection speed increased from 179FPS to 244FPS, achieving a mAP of 0.926. Remarkable advancements across multiple metrics make it well-suited for deploying deep detection models in complex industrial environments.
Study on the correlation between serum IgG4/IgG levels and the development of Graves’ ophthalmopathy
Porous ground treatments for propeller noise reduction in ground effect
Abstract This study investigates the aerodynamic and aeroacoustic behavior of propellers operating in ground-effect conditions, with an emphasis on the impact of porous ground surface treatments. The investigation explores the potential of porous materials to reduce propeller noise near the ground, a major barrier to the acceptance and integration of Urban Air Mobility (UAM) systems. Experiments were conducted in an anechoic chamber using an APC $$10 \times 5.5$$ inch propeller in a pusher configuration. The setup used a rigid flat plate to act as the ground plane at various distances from the propeller. The ground plane was treated with three types of porous foams, each with different pore densities and thicknesses. Noise measurements were taken using a polar array of microphones positioned in both near-field and far-field locations. The results show that porous surface treatments significantly enhance noise suppression. Coherence analysis revealed that porous treatments improve the spatial consistency of acoustic signals, making noise propagation more predictable and controllable. The study also highlights that the interaction between wake flow and porous surfaces leads to greater noise suppression and stability in the hydrodynamic pressure field. These findings have significant implications for designing quieter, more efficient UAM vehicles, aiding their integration into urban environments.